1. Field of the Invention
The present invention is directed to computer systems; and more particularly, it is directed to the processing of digital images.
2. Description of the Related Art
Digital images may include raster graphics, vector graphics, or a combination thereof. Raster graphics data (also referred to herein as bitmaps) may be stored and manipulated as a grid of individual picture elements called pixels. A bitmap may be characterized by its width and height in pixels and also by the number of bits per pixel. Commonly, a color bitmap defined in the RGB (red, green blue) color space may comprise between one and eight bits per pixel for each of the red, green, and blue channels. An alpha channel may be used to store additional data such as per-pixel transparency values. Vector graphics data may be stored and manipulated as one or more geometric objects built with geometric primitives. The geometric primitives (e.g., points, lines, polygons, Bézier curves, and text characters) may be based upon mathematical equations to represent parts of digital images.
Digital image processing is the process of analyzing and/or modifying digital images using a computing device, e.g., a computer system. Using specialized software programs, digital images may be manipulated and transformed in a variety of ways. For example, image scaling is the process of resizing a digital image. Scaling is a process that involves trade-offs among computational efficiency, image smoothness, and image sharpness. As the size of an image is increased, the pixels in the image become increasingly visible, making the image appear “soft.” Conversely, reducing the image in size will tend to enhance its smoothness and apparent sharpness.
Single image super-resolution or image upscaling is the technique of generating a high-resolution image from a low-resolution input. An image upscaling process may predict a relatively large number of unknown pixel values based on a relatively small number of input pixels. Conventional approaches to the problem of upscaling may thus depend on the quality of available models referred to as image priors. The image priors used by conventional approaches tend to range from simple “smoothness” priors to more sophisticated statistical priors learned from natural images. For conventional approaches to image upscaling, the most popular and simplest methods are those based on analytical interpolations, e.g., a bicubic or bilinear interpolation with an analytical “smoothness” assumption.
The process of upscaling an image may introduce noise or magnify the existing noise in the image. Image denoising is the technique of removing noise from an image.